---
title: "Data_Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    source: embed
---

```{r setup, include=FALSE}
library(tidyverse)
library(p8105.datasets)
library(plotly)

library(flexdashboard)
```

Column {data-width=650}
-----------------------------------------------------------------------

### Dot plot - What products have the largest volume of orders and what department are they from?

```{r}
data("instacart")

product_max =
  instacart %>% 
  select(product_name, department) %>% 
  group_by(product_name, department) %>% 
  count(product_name) %>% 
  arrange(desc(n)) %>%
  filter(n > 2300) %>%
  mutate(product_name = fct_reorder(product_name, n)) %>% 
  ggplot(aes(x = product_name, y = n, color = department)) +
  geom_point(alpha = .8) +
  labs(
    x = "product",
    y = "total sales"
  ) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

ggplotly(product_max)
```

Column {data-width=350}
-----------------------------------------------------------------------

### Volume of order by time of the day

```{r}
  instacart %>% 
  group_by(department, order_hour_of_day) %>% 
  tally() %>% 
  filter(n > 3000) %>% 
  plot_ly(x = ~order_hour_of_day, y = ~n, color = ~department, type = "bar", colors = "viridis") 
```

### Shortest reorder time

```{r}
  instacart %>% 
  filter(days_since_prior_order < 30,
         department != "missing") %>%
  group_by(department) %>%
  plot_ly(x = ~department, y = ~days_since_prior_order, color = ~department, type = "box", colors = "viridis")
```